Abstract
Gliomas are the most common malignant brain tumors in adults, with Glioblastoma (GBM) being the most agressive subtype. GBM is clinically evaluated with magnetic resonance imaging (MRI) and presents with different growth phenotypes, involving varying degrees of healthy tissue invasion and tumor induced herniation, also known as mass effect. GBM growth in the brain is frequently modeled as a reaction-diffusion process in which varying ratios of diffusion and proliferation coefficients mimic the observed spectrum of growth phenotypes ranging from nodal to diffuse. However, reaction-diffusion models alone are insufficient to explain tumor-induced mass effect on normal peripheral tissues, which is a critical clinical issue.
We propose an analysis method and framework for estimating GBM growth properties (proliferation, invasiveness, displacive potential) from MRI data routinely collected in the clinical management of GBM. This framework accounts for the mass-effect of the growing tumor by assuming a coupling between local tumor-cell density and volumetric expansion of the tissue.
We evaluate the reconstruction workflow on synthetic data that represents a range of realistic growth situations and levels of uncertainty. For most parameter combinations (90%) that correspond to tumors detectable by T1-weighted MRI, target parameters are recovered with a relative error of less than 15%.
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Acknowledgement
The research leading to these results has received funding from the European Union’s Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant agreement No. 753878.
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Abler, D., Büchler, P., Rockne, R.C. (2019). Towards Model-Based Characterization of Biomechanical Tumor Growth Phenotypes. In: Bebis, G., Benos, T., Chen, K., Jahn, K., Lima, E. (eds) Mathematical and Computational Oncology. ISMCO 2019. Lecture Notes in Computer Science(), vol 11826. Springer, Cham. https://doi.org/10.1007/978-3-030-35210-3_6
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DOI: https://doi.org/10.1007/978-3-030-35210-3_6
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